Compact workload representation of memory system

ABSTRACT

Compact representation for input workloads is generated in a memory system. The memory system includes a memory device; and a controller including a recurrent neural network coder. The recurrent neural network coder includes an encoder including recurrent encoding blocks. Each recurrent encoding block: receives one of the input commands in an input workload associated with the memory device; and generates a hidden state vector corresponding to the received input command by applying a set of activation functions on the received input command. A last encoding block generates a final hidden state vector as the compact representation vector.

BACKGROUND 1. Field

Embodiments of the present disclosure relate to a scheme for analyzing workloads in a memory system.

2. Description of the Related Art

The computer environment paradigm has shifted to ubiquitous computing systems that can be used anytime and anywhere. As a result, the use of portable electronic devices such as mobile phones, digital cameras, and notebook computers has rapidly increased. These portable electronic devices generally use a memory system having memory device(s), that is, data storage device(s). The data storage device is used as a main memory device or an auxiliary memory device of the portable electronic devices.

Memory systems using memory devices provide excellent stability, durability, high information access speed, and low power consumption, since they have no moving parts. Examples of memory systems having such advantages include universal serial bus (USB) memory devices, memory cards having various interfaces such as a universal flash storage (UFS), and solid state drives (SSDs). Memory systems may perform operations associated with one or more workloads from a host. Workload analysis becomes important for performance and reliability improvements in memory systems. In this context, embodiments of the invention arise.

SUMMARY

Aspects of the present invention include a system and a method for compact representation of input workloads in a memory system, capable of separating workloads by certain features.

In one aspect, a system includes a memory device; and a controller including a recurrent neural network coder, which includes an encoder including a plurality of recurrent encoding blocks, which includes first to last encoding blocks. Each recurrent encoding block is configured to: receive one of a plurality of input commands in an input workload associated with the memory device; and generate a hidden state vector corresponding to the received input command by applying a set of activation functions on the received input command. The last encoding block generates a final hidden state vector as the compact representation vector.

In another aspect, a method for operating a controller of a memory system includes: providing a recurrent neural network coder which includes an encoder including a plurality of recurrent encoding blocks; receiving, by each recurrent encoding block, one of a plurality of input commands in an input workload associated with a memory device; and generating, by each recurrent encoding block, a hidden state vector corresponding to the received input command by applying a set of activation functions on the received input command. A last encoding block among the plurality of recurrent encoding blocks generates a final hidden state vector as a compact representation vector corresponding to the plurality of input commands.

Additional aspects of the present invention will become apparent from the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a data processing system in accordance with an embodiment of the present invention.

FIG. 2 is a block diagram illustrating a memory system in accordance with an embodiment of the present invention.

FIG. 3 is a circuit diagram illustrating a memory block of a memory device in accordance with an embodiment of the present invention.

FIG. 4 is a diagram illustrating a data processing system in accordance with an embodiment of the present invention.

FIG. 5 is a diagram illustrating a recurrent neural network coder in accordance with an embodiment of the present invention.

FIG. 6A is a diagram illustrating a plurality of recurrent blocks in accordance with an embodiment of the present invention.

FIG. 6B is a diagram illustrating a recurrent block in accordance with an embodiment of the present invention.

FIG. 7 is a diagram illustrating details of a recurrent neural network coder of FIG. 5.

FIG. 8 is a diagram illustrating a recurrent neural network coder in accordance with an embodiment of the present invention.

FIG. 9 is a diagram illustrating details of a recurrent neural network coder of FIG. 8.

FIG. 10 is a diagram illustrating data set of workloads which are compacted and visualization by a recurrent neural network coder in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION

Various embodiments are described below in more detail with reference to the accompanying drawings. The present invention may, however, be embodied in different forms and thus should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure is thorough and complete and fully conveys the scope of the present invention to those skilled in the art. Moreover, reference herein to “an embodiment,” “another embodiment,” or the like is not necessarily to only one embodiment, and different references to any such phrase are not necessarily to the same embodiment(s). Throughout the disclosure, like reference numerals refer to like parts in the figures and embodiments of the present invention.

The invention can be implemented in numerous ways, including as a process; an apparatus; a system; a computer program product embodied on a computer-readable storage medium; and/or a processor, such as a processor suitable for executing instructions stored on and/or provided by a memory coupled to the processor. In this specification, these implementations, or any other form that the invention may take, may be referred to as techniques. In general, the order of the steps of disclosed processes may be altered within the scope of the invention. Unless stated other wise, a component such as a processor or a memory described as being suitable for performing a task may be implemented as a general component that is temporarily configured to perform the task at a given time or a specific component that is manufactured to perform the task. As used herein, the term ‘processor’ or the like refers to one or more devices, circuits, and/or processing cores suitable for processing data, such as computer program instructions.

A detailed description of embodiments of the invention is provided below along with accompanying figures that illustrate aspects of the invention. The invention is described in connection with such embodiments, but the invention is not limited to any embodiment. The scope of the invention is limited only by the claims. The invention encompasses numerous alternatives, modifications and equivalents within the scope of the claims. Numerous specific details are set forth in the following description in order to provide a thorough understanding of the invention. These details are provided for the purpose of example; the invention may be practiced according to the claims without some or all of these specific details. For clarity, technical material that is known in technical fields related to the invention has not been described in detail so that the invention is not unnecessarily obscured.

FIG. 1 is a block diagram illustrating a data processing system 2 in accordance with an embodiment of the present invention.

Referring FIG. 1, the data processing system 2 may include a host device 5 and a memory system 10. The memory system 10 may receive a request from the host device 5 and operate in response to the received request. For example, the memory system 10 may store data to be accessed by the host device 5.

The host device 5 may be implemented with any of various types of electronic devices. In various embodiments, the host device 5 may include an electronic device such as a desktop computer, a workstation, a three-dimensional (3D) television, a smart television, a digital audio recorder, a digital audio player, a digital picture recorder, a digital picture player, and/or a digital video recorder and a digital video player. In various embodiments, the host device 5 may include a portable electronic device such as a mobile phone, a smart phone, an e-book, an MP3 player, a portable multimedia player (PMP), and/or a portable game player.

The memory system 10 may be implemented with any of various types of storage devices such as a solid state drive (SSD) and a memory card. In various embodiments, the memory system 10 may be provided as one of various components in an electronic device such as a computer, an ultra-mobile personal computer (PC) (UMPC), a workstation, a net-book computer, a personal digital assistant (PDA), a portable computer, a web tablet PC, a wireless phone, a mobile phone, a smart phone, an e-book reader, a portable multimedia player (PMP), a portable game device, a navigation device, a black box, a digital camera, a digital multimedia broadcasting (DMB) player, a 3-dimensional television, a smart television, a digital audio recorder, a digital audio player, a digital picture recorder, a digital picture player, a digital video recorder, a digital video player, a storage device of a data center, a device capable of receiving and transmitting information in a wireless environment, a radio-frequency identification (RFID) device, as well as one of various electronic devices of a home network, one of various electronic devices of a computer network, one of electronic devices of a telematics network, or one of various components of a computing system.

The memory system 10 may include a memory controller 100 and a semiconductor memory device 200. The memory controller 100 may control overall operation of the semiconductor memory device 200.

The semiconductor memory device 200 may perform one or more erase, program, and read operations under the control of the memory controller 100. The semiconductor memory device 200 may receive a command CMD, an address ADDR and data DATA through input/output lines. The semiconductor memory device 200 may receive power PWR through a power line and a control signalCTRL through a control line. The control signal CTRL may include a command latch enable signal, an address latch enable signal, a chip enable signal, a write enable signal, a read enable signal, as well as other operational signals depending on design and configuration of the memory system 10.

The memory controller 100 and the semiconductor memory device 200 may be integrated in a single semiconductor device such as a solid state drive (SSD). The SSD may include a storage device for storing data therein. When the semiconductor memory system 10 is used in an SSD, operation speed of a host device (e.g., host device 5 of FIG. 1) coupled to the memory system 10 may remarkably improve.

The memory controller 100 and the semiconductor memory device 200 may be integrated in a single semiconductor device such as a memory card. For example, the memory controller 100 and the semiconductor memory device 200 may be so integrated to configure a personal computer (PC) card of personal computer memory card international association (PCMCIA), a compact flash (CF) card, a smart media (SM) card, a memory stick, a multimedia card (MMC), a reduced-size multimedia card (RS-MMC), a micro-size version of MMC (MMCmicro), a secure digital (SD) card, a mini secure digital (miniSD) card, a micro secure digital (microSD) card, a secure digital high capacity (SDHC), and/or a universal flash storage (UFS).

FIG. 2 is a block diagram illustrating a memory system in accordance with an embodiment of the present invention. For example, the memory system of FIG. 2 may depict the memory system 10 shown in FIG. 1.

Referring to FIG. 2, the memory system 10 may include a memory controller 100 and a semiconductor memory device 200. The memory system 10 may operate in response to a request from a host device (e.g., host device 5 of FIG. 1), and in particular, store data to be accessed by the host device.

The memory device 200 may store data to be accessed by the host device.

The memory device 200 may be implemented with a volatile memory device such as a dynamic random access memory (DRAM) and/or a static random access memory (SRAM) or a non-volatile memory device such as a read only memory (ROM), a mask ROM (MROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a ferroelectric random access memory (FRAM), a phase change RAM (PRAM), a magnetoresistive RAM (MRAM), and/or a resistive RAM (RRAM).

The controller 100 may control storage of data in the memory device 200. For example, the controller 100 may control the memory device 200 in response to a request from the host device. The controller 100 may provide data read from the memory device 200 to the host device, and may store data provided from the host device into the memory device 200.

The controller 100 may include a storage 110, a control component 120, which may be implemented as a processor such as a central processing unit (CPU), an error correction code (ECC) component 130, a host interface (I/F) 140 and a memory interface (I/F) 150, which are coupled through a bus 160.

The storage 110 may serve as a working memory of the memory system 10 and the controller 100, and store data for driving the memory system 10 and the controller 100. When the controller 100 controls operations of the memory device 200, the storage 110 may store data used by the controller 100 and the memory device 200 for such operations as read, write, program and erase operations.

The storage 110 may be implemented with a volatile memory such as a static random access memory (SRAM) or a dynamic random access memory (DRAM). As described above, the storage 110 may store data used by the host device in the memory device 200 for the read and write operations. To store the data, the storage 110 may include a program memory, a data memory, a write buffer, a read buffer, a map buffer, and the like.

The control component 120 may control general operation of the memory system 10, and in particular a write operation and a read operation for the memory device 200 in response to a corresponding request from the host device. The control component 120 may drive firmware, which is referred to as a flash translation layer (FTL), to control general operations of the memory system 10. For example, the FTL may perform operations such as logical-to-physical (L2P) mapping, wear leveling, garbage collection, and/or bad block handling. The L2P mapping is known as logical block addressing (LBA).

The ECC component 130 may detect and correct errors in the data read from the memory device 200 during the read operation. The ECC component 130 may not correct error bits when the number of the error bits is greater than or equal to a threshold number of correctable error bits, and instead may output an error correction fail signal indicating failure in correcting the error bits.

In various embodiments, the ECC component 130 may perform an error correction operation based on a coded modulation such as a low density parity check (LDPC) code, a Bose-Chaudhuri-Hocquenghem (BCH) code, a turbo code, a turbo product code (TPC), a Reed-Solomon (RS) code, a convolution code, a recursive systematic code (RSC), a trellis-coded modulation (TCM), or a Block coded modulation (BCM). However, error correction is not limited to these techniques. As such, the ECC component 130 may include any and all circuits, systems or devices for suitable error correction operation.

The host interface 140 may communicate with the host device through one or more of various interface protocols such as a universal serial bus (USB), a multi-media card (MMC), a peripheral component interconnect express (PCI-e or PCIe), a small computer system interface (SCSI), a serial-attached SCSI (SAS), a serial advanced technology attachment (SATA), a parallel advanced technology attachment (PATA), an enhanced small disk interface (ESDI), and/or an integrated drive electronics (IDE).

The memory interface 150 may provide an interface between the controller 100 and the memory device 200 to allow the controller 100 to control the memory device 200 in response to a request from the host device. The memory interface 150 may generate control signals for the memory device 200 and process data under the control of the control component 120. When the memory device 200 is a flash memory such as a NAND flash memory, the memory interface 150 may generate control signals for the memory and process data under the control of the control component 120.

The memory device 200 may include a memory cell array 210, a control circuit 220, a voltage generation circuit 230, a row decoder 240, a page buffer 250 which may be in the form of an array of page buffers, a column decoder 260, and an input and output (input/output) circuit 270. The memory cell array 210 may include a plurality of memory blocks 211 which may store data. The voltage generation circuit 230, the row decoder 240, the page buffer array 250, the column decoder 260 and the input/output circuit 270 may form a peripheral circuit for the memory cell array 210. The peripheral circuit may perform a program, read, or erase operation on the memory cell array 210. The control circuit 220 may control the peripheral circuit.

The voltage generation circuit 230 may generate operation voltages of various levels. For example, in an erase operation, the voltage generation circuit 230 may generate operation voltages of various levels such as an erase voltage and a pass voltage.

The row decoder 240 may be in electrical communication with the voltage generation circuit 230, and the plurality of memory blocks 211. The row decoder 240 may select at least one memory block among the plurality of memory blocks 211 in response to a row address generated by the control circuit 220, and transmit operation voltages supplied from the voltage generation circuit 230 to the selected memory blocks.

The page buffer 250 may be coupled with the memory cell array 210 through bit lines BL (shown in FIG. 3). The page buffer 250 may precharge the bit lines BL with a positive voltage, transmit data to, and receive data from, a selected memory block in program and read operations, or temporarily store transmitted data, in response to page buffer control signal(s) generated by the control circuit 220.

The column decoder 260 may transmit data to, and receive data from, the page buffer 250 or transmit and receive data to and from the input/output circuit 270.

The input/output circuit 270 may transmit to the control circuit 220 a command and an address, received from an external device (e.g., the memory controller 100 of FIG. 1), transmit data from the external device to the column decoder 260, or output data from the column decoder 260 to the external device, through the input/output circuit 270.

The control circuit 220 may control the peripheral circuit in response to the command and the address.

FIG. 3 is a circuit diagram illustrating a memory block of a semiconductor memory device in accordance with an embodiment of the present invention. For example, the memory block of FIG. 3 may be any of the memory blocks 211 of the memory cell array 210 shown in FIG. 2.

Referring to FIG. 3, the memory block 211 may include a plurality of word lines WL0 to WLn-1, a drain select line DSL and a source select line SSL coupled to the row decoder 240, These lines may be arranged in parallel, with the plurality of word lines between the DSL and SSL.

The memory block 211 may further include a plurality of cell strings 221 respectively coupled to bit lines BL0 to BLm-1. The cell is string of each column may include one or more drain selection transistors DST and one or more source selection transistors SST. In the illustrated embodiment, each cell string has one DST and one SST. In a cell string, a plurality of memory cells or memory cell transistors MC0 to MCn-1 may be serially coupled between the selection transistors DST and SST. Each of the memory cells may be formed as a single level cell (SLC) storing 1 bit of data, a multi-level cell (MLC) storing 2 bits of data, a triple-level cell (TLC) storing 3 bits of data, or a quadruple-level cell (QLC) storing 4 bits of data.

The source of the SST in each cell string may be coupled to a common source line CSL, and the drain of each DST may be coupled to the corresponding bit line. Gates of the SSTs in the cell strings may be coupled to the SSL, and gates of the DSTs in the cell strings may be coupled to the DSL. Gates of the memory cells across the cell strings may be coupled to respective word lines. That is, the gates of memory cells MC0 are coupled to corresponding word line WL0, the gates of memory cells MC1 are coupled to corresponding word line WL1, etc. The group of memory cells coupled to a particular word line may be referred to as a physical page. Therefore, the number of physical pages in the memory block 211 may correspond to the number of word lines.

The page buffer array 250 may include a plurality of page buffers 251 that are coupled to the bit lines BL0 to BLm-1, The page buffers 251 may operate in response to page buffer control signals. For example, the page buffers 251 my temporarily store data received through the bit lines BL0 to BLm-1 or sense voltages or currents of the bit lines during a read or verify operation.

In some embodiments, the memory blocks 211 may include NAND-type flash memory cells. However, the memory blocks 211 are not limited to such cell type, but may include NOR-type flash memory cells. Memory cell array 210 may be implemented as a hybrid flash memory in which two or more types of memory cells are combined, or one-NAND flash memory in which a controller is embedded inside a memory chip.

FIG. 4 is a diagram illustrating a data processing system 2 in accordance with an embodiment of the present invention.

Referring to FIG. 4, the data processing system 2 may include a host 5 and a memory system 10. The memory system 10 may include a controller 100 and a memory device 200. The controller 100 may include firmware (FW) which is a specific class of software for controlling various operations (e.g., read, write, and erase operations) for the memory device 200. In some embodiments, the firmware may reside in the storage 110 and may be executed by the control component 120, in FIG. 2.

The memory device 200 may include a plurality of memory cells (e.g., NAND flash memory cells). The memory cells are arranged in an array of rows and columns as shown in FIG. 3. The cells in a particular row are connected to a word line (e.g., WL0), while the cells in a particular column are coupled to a bit line (e.g., BL0). These word and bit lines are used for read and write operations.

During a write operation, the data to be written (‘1’ or ‘0’) is provided at the bit line while the word line is asserted. During a read operation, the word line is again asserted, and the threshold voltage of each cell can then be acquired from the bit line. Multiple pages may share the memory cells that belong to (i.e., are coupled to) the same word line.

In the memory system 10, the controller 100 may perform operations associated with one or more workloads from the host 5. Workloads may be defined as data streams (or commands) generated by applications of the host 5 that are seen by the memory system 10 as a collection of access patterns. The memory system 10 may be implemented with a solid state drive (SSD). SSDs will take around 85% of enterprise storage capacities by 2026 according to the report: David McIntyre “Annual Flash Controller Update,” Flash Memory Summit 2019 Proceedings (FMS'19). Flash-based storage is also more beneficial in terms of cost per bit compared to hard disk drives (HDDs). However, due to the limited number of erase/write (EW) cycles and complicated logical to physical (L2P) mapping, flash memory devices in an SSD are much more sensitive to changes of input workload. Therefore, workload analysis becomes important for performance and reliability improvements in flash memory devices (e.g., NAND flash memories).

The workload for SSDs is not limited only to spatial characteristics (i.e., random or sequential access patterns), therefore it may be described using multiple features which are determined based on the developers' experience. For example, the workload analysis process may rely on multiple features such as the number of commands and their duration, characteristics of access segments, input and output requests, intervals between requests, etc. However, even the most exhaustive set of features might not take into account important workload characteristics. Also, some of the features might be irrelevant to the flash translation layer (FTL) algorithms and should be excluded from consideration. To resolve these issues, embodiments of the invention arise. Accordingly, embodiments provide a compact representation scheme for input workloads in a memory system (e.g., SSD such as NAND flash memory devices), capable of separating workloads by certain features.

In accordance with embodiments, the controller 100 of FIG. 4 may provide a coder (i.e., encoder and decoder) for compact representation of an input workload, and an encoding and decoding method thereof. Since workload can be represented as a time series and further considered as sequential data, recurrent neural networks are supposed to be efficient in processing this data. Also, the internal representation of the workload should be compact to be stored in a storage (e.g., volatile memory). Therefore, embodiments provide compact work representation based on a recurrent neural network model and a recurrent neural network (RNN) autoencoder has been used as a model to represent input workload, i.e., a coder including an encoder and a decoder. The quality of the model depends on the diversity and the size of a training set. Introductions of the recurrent neural network model and the recurrent neural network (RNN) autoencoder are described in: T. Mikolov et. al., “Linguistic Regularities in Continuous Space Word Representation,” Proceedings of the 2013 Conference of the North American Chapter of Association for Computational Linguistics, pp. 746-751; and N. Srivastava et. al., “Unsupervised Learning of Video Representations Using LSTMs,” Proceedings of the 32nd international Conference on Machine Learning, PMLR 37:843-852, 2015, which are incorporated by references herein in their respective entireties. Compact representation of the input workload is helpful for PTL reconfiguration and more precise FW parameters tuning. If the data set for training is wide and diverse, embodiments would be able to detect many types of different workloads.

FIG. 5 is a diagram illustrating a recurrent neural network coder 500 in accordance with an embodiment of the present invention. In some cases, the recurrent neural network coder 500 may provide compact workload representation and be referred to as recurrent neural network (RNN) autoencoder.

Referring to FIG. 5, the recurrent neural network coder 500 may include an encoder 510, a storage 520 and a decoder 530. The encoder 510 may receive input workload including a plurality of input commands associated with a memory device (e.g., the memory device 200 of FIG. 4.) For example, the input workload is received from a host 5 of FIG. 4. In some embodiments, the workload may be represented as time series including N commands C₁ to C_(N). Each command C_(i) (where 1≤i≤N) may have at least two features, namely Type of a command (T_(i)), Logic Block Address (LBA_(i)). Features are not limited to the two features above. T_(i) is an integer number corresponding to possible commands. For example, T_(i) may have a value of 0 for read command, a value of 1 for write command, and a value of 2 for erase command. LBA_(i) is also an integer number from a range of possible addresses. For example, LBA_(i) may be an integer number from a range from 0 to 2³²-1.

The encoder 510 may generate a compact representation vector corresponding to the plurality of input commands using a set activation functions. The storage 520 may be coupled to the encoder 510 and may store the compact representation vector. The decoder 530 may be coupled to the storage 520 and may receive the compact representation vector from the storage 520. The decoder 530 may generate recovered commands based on the compact representation vector and the plurality of input commands. Details of the encoder 510, the storage 520 and the decoder 530 are described with reference to FIGS. 6A to 7 below.

Each of the encoder 510 and the decoder 530 may be implemented with a plurality of recurrent blocks 601-60N including a first recurrent block RB₁ to an Nth recurrent block RB_(N) as shown in FIG. 6A. The first recurrent block RB₁ to the Nth recurrent block RBN are connected in cascade.

FIG. 6B is a diagram illustrating a general structure of a recurrent block in FIG. 6A.

Referring to FIG. 6B, a recurrent block RB with index i (RB_(i)) has two inputs and two outputs. Two inputs include a hidden state vector h_(i-1) which corresponds to a hidden state of previous unit RB_(i-1) and an input command vector X_(i) which corresponds to an element of an input sequence. Two outputs include a hidden state vector h_(i) which corresponds to a hidden state of the RB_(i) and an output vector Y_(i) which corresponds to an output value of RB_(i). In some embodiments, vector X_(i) corresponds to commands of input workload and vector Y_(i) corresponds to the recovered or predicted commands for the input workload. Each RB shares three weight matrices W_(Y), W_(h) and W_(X). The outputs of RB are computed as shown in List1:

List1:

1. X_(i)W_(X); 2. ƒ(X_(i)W_(X)), where ƒ is an activation function 3. h_(i-1)W_(h); 4. ƒ(h_(i-1)W_(h)), activation function may differ from the previous one; 5. h_(i)=ƒ(ƒ(X_(i)W_(X))+ƒ(h_(i-1)W_(h))—hidden state; 6. h_(i)W_(Y); 7. Y_(i)=ƒ(h_(i)W_(Y))—recovered or predicted command;

Referring to List1, an activation function ƒ(X_(i)W_(X)) on a combination X_(i)W_(X) of a first input command X_(i) and a first weight matrix W_(X) is performed to generate a first vector. In some embodiments, the activation function includes one of hyperbolic tangent function

${{f(x)} = {{\tanh\; x} = \frac{e^{x} - e^{- x}}{e^{x} + e^{- x}}}},$

sigmoid function

${S(x)} = {\frac{1}{1 + e^{- x}} = \frac{e^{x}}{e^{x} + 1}}$

and rectified linear unit (ReLU) function ƒ(x)=x⁴=max(0, x).

The activation function ƒ(h_(i-1)W_(h)) on a combination of a previous hidden state vector h_(i-1) and a second weight matrix W_(h) is performed to generate a second vector. In some embodiments, the activation function herein may be different from the previous activation function.

The activation function ƒ(ƒ(X_(i)W_(X))+ƒ(h_(i-1)W_(h)) on a sum of the first and second vectors ƒ(X_(i)W_(X))+ƒ(h_(i-1)W_(h)) may be performed to generate a hidden state vector h_(i).

The activation function ƒ(h_(i)W_(Y)) on a combination of the hidden state vector h_(i) and a third weight matrix W_(Y) may generate an output vector Y_(i)=ƒ(h_(i)W_(Y)).

In some embodiments, the described RB may be implemented in different ways including a gated recurrent unit (GRU) and/or long short-term memory (LSTM) as described in J. Chung et. al., “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,” NIPS 2014 Workshop on Deep Learning, December 2014.

FIG. 7 is a diagram illustrating details of a recurrent neural network coder of FIG. 5.

Referring to FIG. 7, the recurrent neural network coder may include an encoder 510, a storage 520 and a decoder 530 and be implemented with 2N recurrent blocks (RBs) in order to generate a compact representation vector R from the input workload.

The encoder 510 may learn an internal representation vector R of a workload including N commands C₁-C_(N). In some embodiments, some of the workloads may be shorter or longer than N commands, therefore the workload should be either padded (i.e., extra dummy commands are added to the workload) or clipped (i.e., last commands are removed from the workload). The workload may be transformed into the compact representation R using N recurrent blocks (RBs) with weight matrices W^(e) _(X), W^(e) _(h), W^(e) _(Y). Each RB may receive two inputs (i.e., a previous hidden state vector h^(e) _(i-1) from a previous recurrent block and command C_(i)) and may output two values (i.e., command Ĉ_(i) and a weighted hidden state vector h^(e) _(i)). Matrix W^(e) _(Y) may be not trainable as the encoder 510 accumulates the information from the input workload and does not predict or recover any commands. As a result, output commands are always equal to zero vectors.

In the illustrated example of FIG. 7, the encoder 510 may include a plurality of recurrent encoding blocks RB^(e) ₁-RB^(e) _(N). Each recurrent encoding block is configured to receive one C_(i) of the plurality of input commands, and generate a hidden state vector h^(e) _(i) corresponding to the received input command by applying the set of activation functions f on the received input command. The last encoding block RB^(e) _(N) among the plurality of recurrent encoding blocks may generate a final hidden state vector h^(e) _(N) as the compact representation vector R.

The first encoding block RB^(e) ₁ is configured to: receive a first input command C₁ among the plurality of input commands; perform the activation function ƒ on a combination of the first input command C₁ and a first weight matrix W^(e) _(X) to generate a first vector ƒ(C₁W^(e) _(X)); perform the activation function on a combination of an initial hidden state vector h^(e) ₀ and a second weight matrix W^(e) _(h) to generate a second vector ƒ(h^(e) ₀W^(e) _(h)); perform the activation function on a sum of the first and second vectors to generate a first hidden state vector h^(e) ₁; and perform the activation function on a combination of the first hidden state vector and a third weight matrix W^(e) _(Y) to generate a first output vector Ĉ₁. In some embodiments, the initial hidden state vector h^(e) ₀ may have a value of 0 and the first output vector Ĉ₁ may have a value of 0.

The second encoding block RB^(e) ₂ is configured to: receive a second input command C₂ among the plurality of input commands; perform the activation function ƒ on a combination of the second input command C₂ and the first weight matrix W^(e) _(X) to generate a third vector ƒ(C₂W^(e) _(X)); perform the activation function on a combination of the first hidden state vector h^(e) ₁ and the second weight matrix W^(e) _(h) to generate a fourth vector ƒ(h^(e) ₁W^(e) _(h)); perform the activation function on a sum of the third and fourth vectors to generate a second hidden state vector h^(e) ₂; and perform the activation function on a combination of the second hidden state vector h^(e) ₂ and the third weight matrix W^(e) _(Y) to generate a second output vector Ĉ₂. In some embodiments, the second output vector Ĉ₂ may have a value of 0.

Remaining encoding blocks among the plurality of recurrent encoding blocks may perform operations similar to the first encoding block RB^(e) ₁ and the second encoding block RB^(e) ₂. The last encoding block RB^(e) _(N) among the plurality of recurrent encoding blocks may generate a final hidden state vector h^(e) _(N) as the compact representation vector R. The compact representation vector R may be stored in the storage 520. In some embodiments, the storage 520 may be a volatile memory such as a random access memory (RAM).

The decoder 530 may receive the compact representation vector R from the storage 520 and generate recovered commands Ĉ₁-Ĉ_(N) based on the compact representation vector R and the plurality of input commands C₁-C_(N). The decoder 530 may recover workload commands Ĉ₁. Ĉ₂, . . . , Ĉ_(N), which may not be exactly the same as the original workload. The structure of the decoder 530 may be symmetric to the encoder 510 but have a cascade connection structure in the opposite order in terms of input commands. The decoder 530 may process the compact representation vector R with N RB units with weight matrices W^(d) _(X), W^(d) _(h), W^(d) _(Y), but in descending order fror the last to the first commands. That is, the last decoding block RB^(d) _(N) among the plurality of decoding blocks RB^(d) ₁-RB^(d) _(N) may be positioned in the first position of the decoder 530 and the first decoding block RB^(d) ₁ may be positioned in the last position of the decoder 530. In some embodiments, the first input command C_(N) may be the same as a zero vector as this command should be recovered from the compact representation vector R. In some embodiments, weight matrices W^(d) _(X), W^(d) _(h), W^(d) _(Y) are the same as or different from weight matrices W^(e) _(X), W^(e) _(h), W^(e) _(Y), respectively.

In the illustrated example of FIG. 7, the last decoding block RB^(d) _(N) is configured to: receive a last input command C_(N) among the plurality of input commands C₁-C_(N) and the compact representation vector R; perform the activation function ƒ on a combination of the last input command C_(N) and a first weight matrix W^(d) _(X) to generate a first vector ƒ(C_(N)W^(d) _(X)); perform the activation function on a combination of the compact representation vector R as an input hidden state vector h^(e) _(N) and a second weight matrix W^(d) _(h) to generate a second vector ƒ(h^(e) _(N)W^(d) _(h)); perform the activation function on a sum of the first and second vectors to generate a last hidden state vector h^(d) _(N); and perform the activation function on a combination of the last hidden state vector h^(d) _(N) and a third weight matrix W^(d) _(Y) to generate a last output vector Ĉ_(N).

The second decoding block RB^(d) ₂ is configured to: receive a second input command C₂ among the plurality of input commands C₁-C_(N); perform the activation function ƒ on a combination of the second input command C₂ and the first weight matrix W^(e) _(X) to generate a third vector ƒ(C₂W^(d) _(X)); perform the activation function on a combination of a second hidden state vector h^(d) ₃, which are received from a third decoding block RB^(d) ₃, and the second weight matrix W^(d) _(h) to generate a fourth vector ƒ(h^(d) ₃W^(d) _(h)); perform the activation function on a sum of the third and fourth vectors to generate a second hidden state vector h^(d) ₂; and perform the activation function on a combination of the second hidden state vector and the third weight matrix to generate a second output vector Ĉ₂.

The first decoding block RB^(d) ₁ is configured to: receive a first input command C₁ among the plurality of input commands C₁-C_(N); perform the activation function ƒ on a combination of the first input command C₁ and the first weight matrix W^(d) _(X) to generate a third vector ƒ(C₁W^(d) _(X)); perform the activation function on a combination of a second hidden state vector h^(d) ₂, which are received from the second decoding block RB^(d) ₂, and the second weight matrix W^(d) _(h) to generate a fourth vector ƒ(h^(d) ₂W^(d) _(h)); perform the activation function on a sum of the third and fourth vectors to generate a first hidden state vector h^(d) ₁; and perform the activation function on a combination of the first hidden state vector and the third weight matrix to generate a first output vector Ĉ₁.

The model of the RNN coder in FIG. 7 may be trained using a data set containing M workloads, which may have different characteristics. The training process may tune weighting matrices W^(e) _(X), W^(e) _(h), W^(e) _(Y), W^(d) _(X), W^(d) _(h), W^(d) _(Y) such that the difference between the source workload (C₁, C₂, . . . , C_(N)) and the recovered workload Ĉ₁, Ĉ₂, . . . , Ĉ_(N)) is minimized. Different optimization algorithms such as Gradient descent, RMSProp, Adam, etc. may be used in the training process. The model may have two hyperpara meters N and d. N represents the number of RBs in the encoder and decoder and d represents the dimension of target compact workload representation vector R.

FIG. 8 is a diagram illustrating a recurrent neural network coder 500 in accordance with an embodiment of the present invention.

Referring to FIG. 8, the recurrent neural network coder 500 may include a predictor 540 in addition to the encoder 510, the storage 520 and the decoder 530 in FIG. 5. The predictor 540 may predict the next K commands following the input workload and generate predicted commands.

FIG. 9 is a diagram illustrating details of a recurrent neural network coder 500 of FIG. 8. The encoder 510 and the decoder 530 in the recurrent neural network coder 500 may have the same structure as shown in FIG. 7.

Referring to FIG. 9, the predictor 540 may include a plurality of recurrent predicting blocks RB^(p) ₁-RB^(p) _(K). The predictor 540 may receive the compact representation vector R and generate predicted commands Ĉ_(N+1)-Ĉ_(N+K) based on the compact representation vector R and a last input command C_(N) among the plurality of input commands.

The plurality of recurrent predicting blocks may include first to last predicting blocks RB^(p) ₁-RB^(p) _(K), which have a cascade connection structure in ascending order, i.e., from first to last predicting blocks.

The first predicting block RB^(p) ₁ is configured to: receive a last input command C_(N) among the plurality of input commands and the compact representation vector R from the storage 520; perform the activation function ion a combination of the last input command Ĉ_(N) (=C_(N)) and a first weight matrix W^(p) _(X) to generate a first vector ƒ(Ĉ_(N)W^(p) _(X)); perform the activation function ƒ on a combination of the compact representation vector R (=h^(e) _(N)) and a second weight matrix W^(p) _(h) to generate a second vector ƒ(h^(e) _(N)W^(p) _(h)); perform the activation function on a sum of the first and second vectors to generate a first hidden state vector h^(p) ₁; and perform the activation function on a combination of the first hidden state vector hand a third weight matrix W^(p) _(Y) to generate a first output vector Ĉ_(N+1) as a first predicted command following the last input command.

The second predicting block RB^(p) ₂ is configured to: receive the first predicted command Ĉ_(N+1) and the first hidden state vector h^(p) ₁; perform the activation function ƒ on a combination of the first predicted command Ĉ_(N+1) and the first weight matrix W^(p) _(X) to generate a third vector ƒ(Ĉ_(N+1)W^(p) _(X)) perform the activation function on a combination of the first hidden state vector h^(p) ₁ and a second weight matrix W^(p) _(h) to generate a fourth vector ƒ(h^(p) ₁W^(p) _(h)); perform the activation function on a sum of the third and fourth vectors to generate a second hidden state vector h^(p) ₂; and perform the activation function on a combination of the second hidden state vector h^(p) ₂ and the third weight matrix W^(p) _(Y) to generate a second output vector Ĉ_(N+2) as a second predicted command following the first predicted command Ĉ_(N+1).

Remaining predicting blocks among the plurality of recurrent predicting blocks may perform operations similar to the first predicting block RB^(p) ₁ and the second predicting block RB^(p) ₂. The last predicting block RB^(p) _(K) among the plurality of recurrent predicting blocks may generate a final hidden state vector h^(p) _(K).

As such, the predictor 540 requires additional K RBs and weight matrices W^(p) _(X), W^(p) _(h), W^(p) _(Y). In some embodiments, weight matrices W^(p) _(X), W^(p) _(h), W^(p) _(Y) are the same as or different from weight matrices W^(e) _(X), W^(e) _(h), W^(e) _(Y), respectively, The output of the predictor 540 is K next commands Ĉ_(N+1)-Ĉ_(N+K) following commands of the input workload C₁-C_(N). Each predicted command is fed into the next RB as an input command. As a result, the next K commands Ĉ_(N+1)-Ĉ_(N+K) following the input workload are predicted and generated.

Examples of implementation of the recurrent neural network (RNN) coder in accordance with embodiments are described below.

It is noted that the recurrent neural network (RNN) coder for compact workload representation has been implemented using long short-term memory (LSTM) recurrent blocks and tested on the data set containing M=900 synthetic workloads N=10,000 commands each.

The data set has 9 types (100 samples each) of workloads which are generated based on two parameters: queue depth (QD) and read/write ratio (RWR) which represents the ratio of read and write commands in the workload. All workloads are random and 9 workload types are shown in List2:

List2:

1. W₁: QD=1, RWR=0/100; 2. W₂: QD=1, RWR=100/0; 3. W₃: QD=1, RWR=70/30; 4. W₄: QD=32, RWR=0/100; 5. W₅: QD=32, RWR=100/0; 6. W₆: QD=32, RWR=70/30; 7. W₇: QD=256, RWR=0/100; 8. W₈: QD=256, RWR=100/0; and 9. W₉: QD=256, RWR=70/30,

The workloads in the data set have been represented as d=25-dimensional vectors. These vectors have been processed by a dimensionality reduction algorithm (e.g., t-SNE algorithm) in order to visualize them in the three-dimensional (3-D) space. One implementation of t-SNE algorithm is described in Laurens van der Maaten, and Geoffrey Hinton, “Visualizing Data using t-SNE,” Journal of Machine Learning Research 9, pp. 2579-2605, 2008. For clarity, only 90 points have been plotted (10 from each workload type) as another 810 points would prevent graphical separation between points due to high density for each workload type. The data set of workloads which are compacted and visualization by a recurrent neural network coder is shown in FIG. 10.

Examples of workloads are transformed into 25-dimensional vector, i.e., compact representation vectors as shown in List3;

List3:

(1) 10,000 write commands are transformed into 25-dimensional vector: (0.0023, 0.0042, 0.0030, −0.0066, 0.0054, −0,0035, 0.0048, −0.0012, 0.0034, −0.0031, 0.0025, −0.0019, −0.0013, −0.0027, 0.0050, 0.0012, 0.0009, −0.0052, 0.0104, 0.0002, 0.0029, −0.0038, −0.0013, −0.0014, 0.0052); (2) 10,000 read commands are transformed into 25-dimensional vector: (−0.0829, 0.0229, 0.2397, −0.0881, −0.0459, 0.1007, 0.0949, −0,1598, −0.0834, −0.1811, 0.1496, −0.1687, −0.0400, −0.2110, −0.1181, 0.0155, 0.1705, −0.0608, 0.1010, −0.1452, 0.0617, −0.0981, 0.1380, 0.0215, 0.2057); and (3) 7,000 read commands and 3,000 write commands are transformed into 25-dimensional vector: (−0.0993, 0.0165, 0.2955, −0.1090, −0.0515, 0.1226, 0.1119, −0.1978, −0.1000, −0.2293, 0.1806, −0.2162, −0.0507, −0.2742, −0.1377, 0.0184, 0.2138, −0.0817, 0.1382, −0.1815, 0.0666, −0.1220, 0.1749, 0.0192, 0.2506).

The average Euclidean distance between the centers of each workload type is presented in Table 1:

TABLE 1 W₁ W₂ W₃ W₄ W₅ W₆ W₇ W₈ W₉ W₁ 0.0000 0.7894 0.5607 0.0005 0.7894 0.5908 0.0003 0.7893 0.5800 W₂ 0.7894 0.0000 0.2307 0.7894 0.0002 0.2002 0.7894 0.0002 0.2109 W₃ 0.5607 0.2307 0.0000 0.5607 0.2307 0.0305 0.5607 0.2307 0.0199 W₄ 0.0005 0.7894 0.5607 0.0000 0.7894 0.5908 0.0002 0.7894 0.5800 W₅ 0.7894 0.0002 0.2307 0.7894 0.0000 0.2002 0.7894 0.0001 0.2109 W₆ 0.5908 0.2002 0.0305 0.5908 0.2002 0.0000 0.5908 0.2002 0.0108 W₇ 0.0003 0.7894 0.5607 0.0002 0.7894 0.5908 0.0000 0.7894 0.5800 W₈ 0.7893 0.0002 0.2307 0.7894 0.0001 0.2002 0.7894 0.0000 0.2102 W₉ 0.5800 0.2109 0.0199 0.5800 0.2109 0.0108 0.5800 0.2102 0.0000

As shown in FIG. 10 and Table 1, workloads are mostly separated by certain read/write ratios (RWRs). For example, (W₁, W₄, W₇) are separated by RWR=0/100, (W₂, W₅, W₈) are separated by RWR=100/0, and (W₃, W₆, W₉) are separated by RWR=70/30. Last group (W₃, W₆, W₉) is more diverse and a queue depth feature is more influential. Thus, it can be seen that the compact workload representation scheme in accordance with embodiments may separate workloads by important features and estimate the difference between input workloads in NAND flash devices.

As described above, embodiments provide the compact workload representation scheme to separate workloads by important features and estimate the difference between input workloads. Embodiments may be helpful for FTL reconfiguration and more precise FW parameters tuning. If the data set for training is wide and diverse, embodiments would be able to detect many types of different workloads.

Although the foregoing embodiments have been illustrated and described in some detail for purposes of clarity and understanding, the present invention is not limited to the details provided. There are many alternative ways of it plementing the invention, as one skilled in the art will appreciate in light of the foregoing disclosure. The disclosed embodiments are thus illustrative, not restrictive. The present invention is intended to embrace all modifications and alternatives that fall within the scope of the appended claims. 

What is claimed is:
 1. A system comprising: a memory device; and a controller including a recurrent neural network coder, which includes an encoder including a plurality of recurrent encoding blocks, which includes first to last encoding blocks, wherein each recurrent encoding block is configured to: receive one of a plurality of input commands in an input workload associated with the memory device; and generate a hidden state vector corresponding to the received input command by applying a set of activation functions on the received input command, and wherein the last encoding block generates a final hidden state vector as the compact representation vector.
 2. The system of claim 1, wherein an activation function among the set of activation functions includes one of a hyperbolic tangent, a sigmoid, and a rectified linear unit.
 3. The system of claim 1, wherein the first to last encoding blocks are connected in cascade.
 4. The system of claim 3, wherein the first encoding block is configured to: receive a first input command among the plurality of input commands; perform the activation function on a combination of the first input command and a first weight matrix to generate a first vector; perform the activation function on a combination of an initial hidden state vector and a second weight matrix to generate a second vector; perform the activation function on a sum of the first and second vectors to generate a first hidden state vector; and perform the activation function on a combination of the first hidden state vector and a third weight matrix to generate a first output vector.
 5. The system of claim 4, wherein a second encoding block among the plurality of recurrent encoding blocks is configured to: receive a second input command among the plurality of input commands; perform the activation function on a combination of the second input command and the first weight matrix to generate a third vector; perform the activation function on a combination of the first hidden state vector and the second weight matrix to generate a fourth vector; perform the activation function on a sum of the third and fourth vectors to generate a second hidden state vector; and perform the activation function on a combination of the second hidden state vector and the third weight matrix to generate a second output vector.
 6. The system of claim 1, wherein the recurrent neural network coder further includes: a storage coupled to the last encoding block and configured to store the compact representation vector; and a decoder including a plurality of recurrent decoding blocks and configured to receive the compact representation vector and generate recovered commands based on the compact representation vector and the plurality of input commands.
 7. The system of claim 6, wherein the plurality of recurrent decoding blocks includes first to last decoding blocks, which are symmetric to the first to last encoding blocks and have a cascade connection structure in descending order.
 8. The system of claim 7, wherein the last decoding block is configured to: receive a last input command among the plurality of input commands and the compact representation vector; perform the activation function on a combination of the last input command and a first weight matrix to generate a first vector; perform the activation function on a combination of the compact representation vector and a second weight matrix to generate a second vector; perform the activation function on a sum of the first and second vectors to generate a last hidden state vector; and perform the activation function on a combination of the last hidden state vector and a third weight matrix to generate a last output vector.
 9. The system of claim 8, wherein the first decoding block is configured to: receive a first input command among the plurality of input commands; perform the activation function on a combination of the first input command and the first weight matrix to generate a third vector; perform the activation function on a combination of a second hidden state vector, which is received from a second decoding block, and the second weight matrix to generate a fourth vector; perform the activation function on a sum of the third and fourth vectors to generate a first hidden state vector; and perform the activation function on a combination of the first hidden state vector and the third weight matrix to generate a first output vector.
 10. The system of claim 8, wherein the first weight matrix, the second weight matrix, and the third weight matrix are trained such that a difference between the plurality of input commands and recovered commands is minimized.
 11. The system of claim 6, further comprising: a predictor including a plurality of recurrent predicting blocks and configured to receive the compact representation vector and generate predicted commands based on the compact representation vector and a last input command among the plurality of input commands, wherein the plurality of recurrent predicting blocks includes first to last predicting blocks, which have a cascade connection structure in ascending order.
 12. The system of claim 11, wherein the first predicting block is configured to: receive a last input command among the plurality of input commands and the compact representation vector; perform the activation function on a combination of the last input command and a first weight matrix to generate a first vector; perform the activation function on a combination of the compact representation vector and a second weight matrix to generate a second vector; perform the activation function on a sum of the first vector and the second vector to generate a first hidden state vector; and perform the activation function on a combination of the first hidden state vector and a third weight matrix to generate a first output vector as a first predicted command following the last input command,
 13. The system of claim 12, wherein the second predicting block is configured to: receive the first predicted command and the first hidden state vector; perform the activation function on a combination of the first predicted command and the first weight matrix to generate a third vector; perform the activation function on a combination of the first hidden state vector and the second weight matrix to generate a fourth vector; perform the activation function on a sum of the third vector and the fourth vector to generate a second hidden state vector; and perform the activation function on a combination of the second hidden state vector and the third weight matrix to generate a second output vector as a second predicted command following the first predicted command.
 14. The system of claim 11, wherein the recurrent encoding, decoding and predicting blocks are implemented in different ways.
 15. A method for operating a controller of a memory system, the method comprising: providing a recurrent neural network coder which includes an encoder including a plurality of recurrent encoding blocks; receiving, by each recurrent encoding block, one of a plurality of input commands in an input workload associated with a memory device; and generating, by each recurrent encoding block, a hidden state vector corresponding to the received input command by applying a set of activation functions on the received input command, wherein a last encoding block among the plurality of recurrent encoding blocks generates a final hidden state vector as a compact representation vector corresponding to the plurality of input commands.
 16. The method of claim 15, wherein an activation function among the set of activation functions includes one of a hyperbolic tangent, a sigmoid, and a rectified linear unit, and wherein first to last encoding blocks are connected in cascade.
 17. The method of claim 15, further comprising: providing a storage coupled to the last encoding block and configured to store the compact representation vector, and a decoder including a plurality of recurrent decoding blocks and configured to receive the compact representation vector and generate recovered commands based on the compact representation vector and the plurality of input commands.
 18. The method of claim 17, wherein the plurality of recurrent decoding blocks includes first to last decoding blocks, which are symmetric to the first to last encoding blocks and have a cascade connection structure in descending order.
 19. The method of claim 17, wherein the generating of the hidden state vector corresponding to the received input command includes performing the activation function on a combination of the received input command and set one or more matrices, which are trained such that a difference between the plurality of input commands and the recovered commands is minimized.
 20. The method of claim 17, further comprising: proving a predictor including a plurality of recurrent predicting blocks and configured to receive the compact representation vector and generate predicted commands based on the compact representation vector and a last input command among the plurality of input commands, wherein the plurality of recurrent predicting blocks includes first to last predicting blocks, which have a cascade connection structure in ascending order, and wherein the recurrent encoding, decoding and predicting blocks are implemented in different ways. 